from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 14.0 | 22.520885 |
| daal4py_KNeighborsClassifier | 0.0 | 3.0 | 27.473946 |
| KNeighborsClassifier_kd_tree | 0.0 | 6.0 | 43.563386 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 1.0 | 48.883594 |
| KMeans_tall | 0.0 | 1.0 | 40.918294 |
| daal4py_KMeans_tall | 0.0 | 1.0 | 19.199566 |
| KMeans_short | 0.0 | 0.0 | 19.959288 |
| daal4py_KMeans_short | 0.0 | 0.0 | 9.932689 |
| LogisticRegression | 0.0 | 1.0 | 4.698853 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 57.526663 |
| Ridge | 0.0 | 0.0 | 26.275623 |
| daal4py_Ridge | 0.0 | 0.0 | 6.973181 |
| total | 0.0 | 32.0 | 28.002008 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | n_jobs | n_neighbors | throughput | latency | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 0.133 | 0.007 | 1000000 | 1000000 | 100 | brute | -1 | 1 | 6.033 | NaN | 0.977 | 0.986 | 0.483 | 0.002 | 0.275 | 0.014 | See |
| 1 | KNeighborsClassifier | predict | 25.800 | 0.443 | 1000000 | 1000 | 100 | brute | -1 | 1 | 0.000 | 0.026 | 0.977 | 0.986 | 2.017 | 0.012 | 12.789 | 0.232 | See |
| 2 | KNeighborsClassifier | predict | 0.171 | 0.017 | 1000000 | 1 | 100 | brute | -1 | 1 | 0.005 | 0.000 | 0.977 | 0.986 | 0.086 | 0.001 | 1.997 | 0.201 | See |
| 3 | KNeighborsClassifier | fit | 0.138 | 0.003 | 1000000 | 1000000 | 100 | brute | -1 | 5 | 5.779 | NaN | 0.977 | 0.986 | 0.493 | 0.004 | 0.281 | 0.006 | See |
| 4 | KNeighborsClassifier | predict | 36.624 | 0.000 | 1000000 | 1000 | 100 | brute | -1 | 5 | 0.000 | 0.037 | 0.977 | 0.986 | 2.013 | 0.018 | 18.189 | 0.166 | See |
| 5 | KNeighborsClassifier | predict | 0.188 | 0.019 | 1000000 | 1 | 100 | brute | -1 | 5 | 0.004 | 0.000 | 0.977 | 0.986 | 0.087 | 0.001 | 2.178 | 0.221 | See |
| 6 | KNeighborsClassifier | fit | 0.131 | 0.004 | 1000000 | 1000000 | 100 | brute | -1 | 100 | 6.109 | NaN | 0.977 | 0.986 | 0.491 | 0.002 | 0.267 | 0.007 | See |
| 7 | KNeighborsClassifier | predict | 36.651 | 0.000 | 1000000 | 1000 | 100 | brute | -1 | 100 | 0.000 | 0.037 | 0.977 | 0.986 | 2.073 | 0.014 | 17.683 | 0.123 | See |
| 8 | KNeighborsClassifier | predict | 0.173 | 0.012 | 1000000 | 1 | 100 | brute | -1 | 100 | 0.005 | 0.000 | 0.977 | 0.986 | 0.085 | 0.001 | 2.019 | 0.140 | See |
| 9 | KNeighborsClassifier | fit | 0.126 | 0.001 | 1000000 | 1000000 | 100 | brute | 1 | 1 | 6.369 | NaN | 0.977 | 0.986 | 0.493 | 0.004 | 0.255 | 0.003 | See |
| 10 | KNeighborsClassifier | predict | 13.256 | 0.024 | 1000000 | 1000 | 100 | brute | 1 | 1 | 0.000 | 0.013 | 0.977 | 0.986 | 1.995 | 0.018 | 6.643 | 0.062 | See |
| 11 | KNeighborsClassifier | predict | 0.182 | 0.001 | 1000000 | 1 | 100 | brute | 1 | 1 | 0.004 | 0.000 | 0.977 | 0.986 | 0.085 | 0.001 | 2.138 | 0.026 | See |
| 12 | KNeighborsClassifier | fit | 0.127 | 0.001 | 1000000 | 1000000 | 100 | brute | 1 | 5 | 6.324 | NaN | 0.977 | 0.986 | 0.491 | 0.001 | 0.257 | 0.001 | See |
| 13 | KNeighborsClassifier | predict | 24.833 | 0.023 | 1000000 | 1000 | 100 | brute | 1 | 5 | 0.000 | 0.025 | 0.977 | 0.986 | 1.997 | 0.010 | 12.437 | 0.061 | See |
| 14 | KNeighborsClassifier | predict | 0.193 | 0.003 | 1000000 | 1 | 100 | brute | 1 | 5 | 0.004 | 0.000 | 0.977 | 0.986 | 0.086 | 0.001 | 2.255 | 0.038 | See |
| 15 | KNeighborsClassifier | fit | 0.126 | 0.001 | 1000000 | 1000000 | 100 | brute | 1 | 100 | 6.341 | NaN | 0.977 | 0.986 | 0.494 | 0.004 | 0.255 | 0.003 | See |
| 16 | KNeighborsClassifier | predict | 24.844 | 0.049 | 1000000 | 1000 | 100 | brute | 1 | 100 | 0.000 | 0.025 | 0.977 | 0.986 | 2.076 | 0.016 | 11.970 | 0.094 | See |
| 17 | KNeighborsClassifier | predict | 0.194 | 0.004 | 1000000 | 1 | 100 | brute | 1 | 100 | 0.004 | 0.000 | 0.977 | 0.986 | 0.087 | 0.002 | 2.235 | 0.063 | See |
| 18 | KNeighborsClassifier | fit | 0.061 | 0.001 | 1000000 | 1000000 | 2 | brute | -1 | 1 | 0.263 | NaN | 0.977 | 0.986 | 0.110 | 0.004 | 0.554 | 0.019 | See |
| 19 | KNeighborsClassifier | predict | 22.414 | 0.057 | 1000000 | 1000 | 2 | brute | -1 | 1 | 0.000 | 0.022 | 0.977 | 0.986 | 0.314 | 0.005 | 71.297 | 1.246 | See |
| 20 | KNeighborsClassifier | predict | 0.022 | 0.002 | 1000000 | 1 | 2 | brute | -1 | 1 | 0.001 | 0.000 | 0.977 | 0.986 | 0.006 | 0.001 | 3.423 | 0.458 | See |
| 21 | KNeighborsClassifier | fit | 0.062 | 0.000 | 1000000 | 1000000 | 2 | brute | -1 | 5 | 0.259 | NaN | 0.977 | 0.986 | 0.110 | 0.004 | 0.561 | 0.020 | See |
| 22 | KNeighborsClassifier | predict | 33.966 | 0.000 | 1000000 | 1000 | 2 | brute | -1 | 5 | 0.000 | 0.034 | 0.977 | 0.986 | 0.316 | 0.006 | 107.416 | 2.071 | See |
| 23 | KNeighborsClassifier | predict | 0.036 | 0.002 | 1000000 | 1 | 2 | brute | -1 | 5 | 0.000 | 0.000 | 0.977 | 0.986 | 0.006 | 0.001 | 5.613 | 0.801 | See |
| 24 | KNeighborsClassifier | fit | 0.064 | 0.003 | 1000000 | 1000000 | 2 | brute | -1 | 100 | 0.252 | NaN | 0.977 | 0.986 | 0.108 | 0.001 | 0.587 | 0.027 | See |
| 25 | KNeighborsClassifier | predict | 34.338 | 0.000 | 1000000 | 1000 | 2 | brute | -1 | 100 | 0.000 | 0.034 | 0.977 | 0.986 | 0.367 | 0.002 | 93.634 | 0.469 | See |
| 26 | KNeighborsClassifier | predict | 0.036 | 0.002 | 1000000 | 1 | 2 | brute | -1 | 100 | 0.000 | 0.000 | 0.977 | 0.986 | 0.006 | 0.000 | 5.785 | 0.393 | See |
| 27 | KNeighborsClassifier | fit | 0.069 | 0.005 | 1000000 | 1000000 | 2 | brute | 1 | 1 | 0.231 | NaN | 0.977 | 0.986 | 0.110 | 0.002 | 0.629 | 0.046 | See |
| 28 | KNeighborsClassifier | predict | 10.460 | 0.002 | 1000000 | 1000 | 2 | brute | 1 | 1 | 0.000 | 0.010 | 0.977 | 0.986 | 0.311 | 0.002 | 33.582 | 0.265 | See |
| 29 | KNeighborsClassifier | predict | 0.016 | 0.001 | 1000000 | 1 | 2 | brute | 1 | 1 | 0.001 | 0.000 | 0.977 | 0.986 | 0.006 | 0.001 | 2.468 | 0.222 | See |
| 30 | KNeighborsClassifier | fit | 0.062 | 0.000 | 1000000 | 1000000 | 2 | brute | 1 | 5 | 0.260 | NaN | 0.977 | 0.986 | 0.109 | 0.001 | 0.564 | 0.006 | See |
| 31 | KNeighborsClassifier | predict | 22.354 | 0.038 | 1000000 | 1000 | 2 | brute | 1 | 5 | 0.000 | 0.022 | 0.977 | 0.986 | 0.311 | 0.001 | 71.783 | 0.361 | See |
| 32 | KNeighborsClassifier | predict | 0.030 | 0.001 | 1000000 | 1 | 2 | brute | 1 | 5 | 0.001 | 0.000 | 0.977 | 0.986 | 0.006 | 0.001 | 4.676 | 0.775 | See |
| 33 | KNeighborsClassifier | fit | 0.061 | 0.000 | 1000000 | 1000000 | 2 | brute | 1 | 100 | 0.261 | NaN | 0.977 | 0.986 | 0.109 | 0.001 | 0.560 | 0.006 | See |
| 34 | KNeighborsClassifier | predict | 22.426 | 0.009 | 1000000 | 1000 | 2 | brute | 1 | 100 | 0.000 | 0.022 | 0.977 | 0.986 | 0.371 | 0.003 | 60.420 | 0.468 | See |
| 35 | KNeighborsClassifier | predict | 0.030 | 0.001 | 1000000 | 1 | 2 | brute | 1 | 100 | 0.001 | 0.000 | 0.977 | 0.986 | 0.008 | 0.001 | 3.892 | 0.568 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | n_jobs | n_neighbors | throughput | latency | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 3.316 | 0.025 | 1000000 | 1000000 | 10 | kd_tree | -1 | 1 | 0.024 | NaN | 0.989 | 0.987 | 0.746 | 0.008 | 4.444 | 0.056 | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 0.474 | 0.006 | 1000000 | 1000 | 10 | kd_tree | -1 | 1 | 0.000 | 0.000 | 0.989 | 0.987 | 0.101 | 0.003 | 4.703 | 0.158 | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 0.004 | 0.001 | 1000000 | 1 | 10 | kd_tree | -1 | 1 | 0.021 | 0.000 | 0.989 | 0.987 | 0.000 | 0.000 | 13.855 | 6.411 | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 3.389 | 0.053 | 1000000 | 1000000 | 10 | kd_tree | -1 | 5 | 0.024 | NaN | 0.989 | 0.987 | 0.774 | 0.007 | 4.376 | 0.080 | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 0.915 | 0.009 | 1000000 | 1000 | 10 | kd_tree | -1 | 5 | 0.000 | 0.001 | 0.989 | 0.987 | 0.185 | 0.003 | 4.944 | 0.101 | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 0.005 | 0.001 | 1000000 | 1 | 10 | kd_tree | -1 | 5 | 0.017 | 0.000 | 0.989 | 0.987 | 0.000 | 0.000 | 12.494 | 5.894 | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 3.348 | 0.069 | 1000000 | 1000000 | 10 | kd_tree | -1 | 100 | 0.024 | NaN | 0.989 | 0.987 | 0.749 | 0.011 | 4.472 | 0.115 | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 2.918 | 0.022 | 1000000 | 1000 | 10 | kd_tree | -1 | 100 | 0.000 | 0.003 | 0.989 | 0.987 | 0.565 | 0.008 | 5.162 | 0.079 | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 0.009 | 0.001 | 1000000 | 1 | 10 | kd_tree | -1 | 100 | 0.009 | 0.000 | 0.989 | 0.987 | 0.001 | 0.000 | 11.305 | 4.351 | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 3.281 | 0.019 | 1000000 | 1000000 | 10 | kd_tree | 1 | 1 | 0.024 | NaN | 0.989 | 0.987 | 0.788 | 0.007 | 4.166 | 0.046 | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 0.775 | 0.006 | 1000000 | 1000 | 10 | kd_tree | 1 | 1 | 0.000 | 0.001 | 0.989 | 0.987 | 0.100 | 0.005 | 7.766 | 0.400 | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 10 | kd_tree | 1 | 1 | 0.050 | 0.000 | 0.989 | 0.987 | 0.000 | 0.000 | 6.062 | 2.652 | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 3.294 | 0.041 | 1000000 | 1000000 | 10 | kd_tree | 1 | 5 | 0.024 | NaN | 0.989 | 0.987 | 0.765 | 0.016 | 4.305 | 0.107 | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1.516 | 0.010 | 1000000 | 1000 | 10 | kd_tree | 1 | 5 | 0.000 | 0.002 | 0.989 | 0.987 | 0.181 | 0.003 | 8.352 | 0.150 | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 10 | kd_tree | 1 | 5 | 0.035 | 0.000 | 0.989 | 0.987 | 0.000 | 0.000 | 6.713 | 3.118 | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 3.305 | 0.048 | 1000000 | 1000000 | 10 | kd_tree | 1 | 100 | 0.024 | NaN | 0.989 | 0.987 | 0.785 | 0.009 | 4.212 | 0.078 | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 4.977 | 0.031 | 1000000 | 1000 | 10 | kd_tree | 1 | 100 | 0.000 | 0.005 | 0.989 | 0.987 | 0.562 | 0.005 | 8.854 | 0.094 | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 0.007 | 0.001 | 1000000 | 1 | 10 | kd_tree | 1 | 100 | 0.011 | 0.000 | 0.989 | 0.987 | 0.001 | 0.000 | 9.374 | 3.678 | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 0.883 | 0.010 | 1000000 | 1000000 | 2 | kd_tree | -1 | 1 | 0.018 | NaN | 0.989 | 0.987 | 0.518 | 0.010 | 1.704 | 0.038 | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 0.029 | 0.001 | 1000000 | 1000 | 2 | kd_tree | -1 | 1 | 0.001 | 0.000 | 0.989 | 0.987 | 0.001 | 0.000 | 25.862 | 9.070 | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | kd_tree | -1 | 1 | 0.006 | 0.000 | 0.989 | 0.987 | 0.000 | 0.000 | 20.724 | 15.480 | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 0.885 | 0.011 | 1000000 | 1000000 | 2 | kd_tree | -1 | 5 | 0.018 | NaN | 0.989 | 0.987 | 0.517 | 0.010 | 1.713 | 0.038 | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 0.031 | 0.001 | 1000000 | 1000 | 2 | kd_tree | -1 | 5 | 0.001 | 0.000 | 0.989 | 0.987 | 0.002 | 0.001 | 19.478 | 10.136 | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.001 | 1000000 | 1 | 2 | kd_tree | -1 | 5 | 0.006 | 0.000 | 0.989 | 0.987 | 0.000 | 0.000 | 21.138 | 16.257 | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 0.894 | 0.019 | 1000000 | 1000000 | 2 | kd_tree | -1 | 100 | 0.018 | NaN | 0.989 | 0.987 | 0.512 | 0.006 | 1.747 | 0.042 | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 0.051 | 0.001 | 1000000 | 1000 | 2 | kd_tree | -1 | 100 | 0.000 | 0.000 | 0.989 | 0.987 | 0.008 | 0.001 | 6.668 | 0.680 | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.001 | 1000000 | 1 | 2 | kd_tree | -1 | 100 | 0.006 | 0.000 | 0.989 | 0.987 | 0.000 | 0.000 | 20.610 | 14.752 | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 0.917 | 0.023 | 1000000 | 1000000 | 2 | kd_tree | 1 | 1 | 0.017 | NaN | 0.989 | 0.987 | 0.521 | 0.007 | 1.761 | 0.050 | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 0.026 | 0.000 | 1000000 | 1000 | 2 | kd_tree | 1 | 1 | 0.001 | 0.000 | 0.989 | 0.987 | 0.001 | 0.001 | 24.497 | 14.492 | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 1 | 0.023 | 0.000 | 0.989 | 0.987 | 0.000 | 0.000 | 3.890 | 2.680 | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 0.877 | 0.017 | 1000000 | 1000000 | 2 | kd_tree | 1 | 5 | 0.018 | NaN | 0.989 | 0.987 | 0.524 | 0.006 | 1.673 | 0.038 | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 0.028 | 0.001 | 1000000 | 1000 | 2 | kd_tree | 1 | 5 | 0.001 | 0.000 | 0.989 | 0.987 | 0.001 | 0.001 | 19.306 | 8.398 | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 5 | 0.022 | 0.000 | 0.989 | 0.987 | 0.000 | 0.000 | 5.483 | 3.841 | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 0.891 | 0.019 | 1000000 | 1000000 | 2 | kd_tree | 1 | 100 | 0.018 | NaN | 0.989 | 0.987 | 0.529 | 0.015 | 1.686 | 0.061 | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 0.056 | 0.001 | 1000000 | 1000 | 2 | kd_tree | 1 | 100 | 0.000 | 0.000 | 0.989 | 0.987 | 0.008 | 0.001 | 7.192 | 1.331 | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 100 | 0.022 | 0.000 | 0.989 | 0.987 | 0.000 | 0.000 | 5.624 | 3.889 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | init | max_iter | n_clusters | n_init | tol | n_iter_sklearn | throughput | latency | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 0.653 | 0.016 | 1000000 | 1000000 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.735 | NaN | 0.002 | 30 | 0.001 | 0.497 | 0.021 | 1.313 | 0.064 | See |
| 1 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.011 | 0.0 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 6.687 | 4.234 | See |
| 2 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.011 | 0.0 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 6.765 | 3.464 | See |
| 3 | KMeans_tall | fit | 0.564 | 0.007 | 1000000 | 1000000 | 2 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.851 | NaN | 0.002 | 30 | 0.001 | 0.443 | 0.013 | 1.275 | 0.040 | See |
| 4 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1000 | 2 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.011 | 0.0 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 7.402 | 3.741 | See |
| 5 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.011 | 0.0 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 10.791 | 7.068 | See |
| 6 | KMeans_tall | fit | 6.400 | 0.108 | 1000000 | 1000000 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 3.750 | NaN | 0.002 | 30 | 0.001 | 3.089 | 0.027 | 2.072 | 0.039 | See |
| 7 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.440 | 0.0 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 5.479 | 2.820 | See |
| 8 | KMeans_tall | predict | 0.002 | 0.001 | 1000000 | 1 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.478 | 0.0 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 8.856 | 6.708 | See |
| 9 | KMeans_tall | fit | 6.051 | 0.020 | 1000000 | 1000000 | 100 | full | random | 30 | 3 | 1 | 0.0 | 30 | 3.966 | NaN | 0.002 | 30 | 0.001 | 2.940 | 0.049 | 2.058 | 0.035 | See |
| 10 | KMeans_tall | predict | 0.002 | 0.001 | 1000000 | 1000 | 100 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.356 | 0.0 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 7.762 | 4.380 | See |
| 11 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1 | 100 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.546 | 0.0 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 7.652 | 4.840 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | init | max_iter | n_clusters | n_init | tol | n_iter_sklearn | throughput | latency | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 0.300 | 0.015 | 10000 | 10000 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | 30 | 0.016 | NaN | 0.0 | 30 | 0.007 | 0.111 | 0.004 | 2.704 | 0.172 | See |
| 1 | KMeans_short | predict | 0.002 | 0.001 | 10000 | 1000 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | 30 | 0.007 | 0.0 | 0.0 | 30 | 0.007 | 0.001 | 0.000 | 2.769 | 1.225 | See |
| 2 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | 30 | 0.011 | 0.0 | 0.0 | 30 | 0.007 | 0.000 | 0.000 | 10.197 | 6.659 | See |
| 3 | KMeans_short | fit | 0.133 | 0.005 | 10000 | 10000 | 2 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.036 | NaN | 0.0 | 30 | 0.007 | 0.048 | 0.001 | 2.760 | 0.124 | See |
| 4 | KMeans_short | predict | 0.002 | 0.001 | 10000 | 1000 | 2 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.007 | 0.0 | 0.0 | 30 | 0.007 | 0.001 | 0.000 | 2.980 | 1.014 | See |
| 5 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1 | 2 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.010 | 0.0 | 0.0 | 30 | 0.007 | 0.000 | 0.000 | 10.341 | 6.719 | See |
| 6 | KMeans_short | fit | 0.682 | 0.021 | 10000 | 10000 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | 22 | 0.258 | NaN | 0.0 | 19 | 0.007 | 0.402 | 0.022 | 1.697 | 0.108 | See |
| 7 | KMeans_short | predict | 0.003 | 0.001 | 10000 | 1000 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | 22 | 0.262 | 0.0 | 0.0 | 19 | 0.007 | 0.001 | 0.000 | 2.217 | 0.584 | See |
| 8 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | 22 | 0.517 | 0.0 | 0.0 | 19 | 0.007 | 0.000 | 0.000 | 7.143 | 3.754 | See |
| 9 | KMeans_short | fit | 0.292 | 0.048 | 10000 | 10000 | 100 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.822 | NaN | 0.0 | 25 | 0.007 | 0.184 | 0.024 | 1.589 | 0.333 | See |
| 10 | KMeans_short | predict | 0.003 | 0.000 | 10000 | 1000 | 100 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.281 | 0.0 | 0.0 | 25 | 0.007 | 0.001 | 0.000 | 2.107 | 0.552 | See |
| 11 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1 | 100 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.518 | 0.0 | 0.0 | 25 | 0.007 | 0.000 | 0.000 | 8.544 | 4.798 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | n_iter | throughput | latency | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 11.678 | 0.023 | 1000000 | 1000000 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [20] | [-0.10103401] | NaN | 0.26 | 11.639 | 0.021 | 1.003 | 0.003 | See |
| 1 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [20] | 2.1584973835343684 | 0.0 | 0.26 | 0.000 | 0.000 | 0.868 | 0.403 | See |
| 2 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [20] | 9.857801216392623 | 0.0 | 0.26 | 0.000 | 0.000 | 0.421 | 0.363 | See |
| 3 | LogisticRegression | fit | 0.776 | 0.009 | 1000 | 1000 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [26] | [2.68003413] | NaN | 0.26 | 0.771 | 0.003 | 1.006 | 0.013 | See |
| 4 | LogisticRegression | predict | 0.002 | 0.000 | 1000 | 100 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [26] | 4.452439789320023 | 0.0 | 0.26 | 0.003 | 0.000 | 0.535 | 0.103 | See |
| 5 | LogisticRegression | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [26] | 77.96890987503261 | 0.0 | 0.26 | 0.001 | 0.000 | 0.133 | 0.089 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | n_iter | throughput | latency | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 0.192 | 0.005 | 1000 | 1000 | 10000 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | NaN | 0.416 | NaN | 1.0 | 0.196 | 0.002 | 0.982 | 0.028 | See |
| 1 | Ridge | predict | 0.010 | 0.000 | 1000 | 1000 | 10000 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | NaN | 7.880 | 0.0 | 1.0 | 0.017 | 0.000 | 0.602 | 0.026 | See |
| 2 | Ridge | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | NaN | 1053.966 | 0.0 | 1.0 | 0.000 | 0.000 | 0.643 | 0.608 | See |
| 3 | Ridge | fit | 1.226 | 0.053 | 1000000 | 1000000 | 100 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | NaN | 0.652 | NaN | 1.0 | 0.243 | 0.004 | 5.043 | 0.233 | See |
| 4 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | NaN | 3.193 | 0.0 | 1.0 | 0.000 | 0.000 | 0.737 | 0.829 | See |
| 5 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | NaN | 11.983 | 0.0 | 1.0 | 0.000 | 0.000 | 0.383 | 0.423 | See |
{
"system_info": {
"python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.1",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.2",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": null,
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}